Community detection and stochastic block models: recent developments
E Abbe - Journal of Machine Learning Research, 2018 - jmlr.org
The stochastic block model (SBM) is a random graph model with planted clusters. It is widely
employed as a canonical model to study clustering and community detection, and provides …
employed as a canonical model to study clustering and community detection, and provides …
Spectral methods for data science: A statistical perspective
Spectral methods have emerged as a simple yet surprisingly effective approach for
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
extracting information from massive, noisy and incomplete data. In a nutshell, spectral …
[图书][B] High-dimensional probability: An introduction with applications in data science
R Vershynin - 2018 - books.google.com
High-dimensional probability offers insight into the behavior of random vectors, random
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …
matrices, random subspaces, and objects used to quantify uncertainty in high dimensions …
Community detection in general stochastic block models: Fundamental limits and efficient algorithms for recovery
E Abbe, C Sandon - 2015 IEEE 56th Annual Symposium on …, 2015 - ieeexplore.ieee.org
New phase transition phenomena have recently been discovered for the stochastic block
model, for the special case of two non-overlapping symmetric communities. This gives raise …
model, for the special case of two non-overlapping symmetric communities. This gives raise …
Achieving optimal misclassification proportion in stochastic block models
Community detection is a fundamental statistical problem in network data analysis. In this
paper, we present a polynomial time two-stage method that provably achieves optimal …
paper, we present a polynomial time two-stage method that provably achieves optimal …
Community detection in degree-corrected block models
Community detection in degree-corrected block models Page 1 The Annals of Statistics 2018,
Vol. 46, No. 5, 2153–2185 https://doi.org/10.1214/17-AOS1615 © Institute of Mathematical …
Vol. 46, No. 5, 2153–2185 https://doi.org/10.1214/17-AOS1615 © Institute of Mathematical …
Reducibility and statistical-computational gaps from secret leakage
M Brennan, G Bresler - Conference on Learning Theory, 2020 - proceedings.mlr.press
Inference problems with conjectured statistical-computational gaps are ubiquitous
throughout modern statistics, computer science, statistical physics and discrete probability …
throughout modern statistics, computer science, statistical physics and discrete probability …
Concentration and regularization of random graphs
This paper studies how close random graphs are typically to their expectations. We interpret
this question through the concentration of the adjacency and Laplacian matrices in the …
this question through the concentration of the adjacency and Laplacian matrices in the …